Find research datasets worth reusing

opencc-by-4.0Jun 2010View details →

Figure 2: Optimization in natural ants collective behavior: foraging and clustering (from [8])-Self-organization and social insects algorithms

On figure 2, two examples of self-organization in natural ants are presented. On the left side, the well-known Deneubourg experiment consists to highlight with a very simple device the ant foraging problem. The ant objectives is to find the optimal way from nest to food source, using pheromone trail deposition. On the right side, cemetery clustering formation are shown at 4 successive times: ants form piles of corpses to clean their nests. Each of them has elementary actions, unknowing the whole situation, but dealing only with local information. There is no supervisor to lead the piles formation which emerges from ant interactions.

opencc-by-4.0Jan 2018View details →

Output from the SCOFF clustering of myExperiment Workflow Fragments

Using a semantic-similarity approach, we clustered the annotated, abstracted workflow fragments (see https://doi.org/10.5281/zenodo.1147545  and https://doi.org/10.5281/zenodo.1147485).  This zip file contains the results of that clustering.

Spectral Cluster Supertree: Analysis Data

Contains all datasets used in the Spectral Cluster Supertree paper. The datasets are composed of a set of rooted model trees, and rooted source trees to predict them. Please cite the appropriate papers, depending on which of the datasets you use. The <code>birth_death</code> folder contains our own dataset generated for our paper (where the generation process is explained), it aims to mimic what may be seen through divide and conquer algorithms for phylogenetic reconstruction. Parameters used to simulate an alignment were simulated under parameters estimated from a sequence alignment of 3 bacterial species (Kaehler et al., 2015) - see <code>alignment</code> folder. The <code>SMIDGenOutgrouped</code> folder contains both the SMIDGenOG (Fleischauer and Böcker, 2016) and SMIDGenOG-5500 dataset (Fleischauer and Böcker, 2017). The <code>SuperTriplets</code> folder contains the SuperTriplets dataset (Ranwez et al, 2010).

UAV-derived cluster greenness and pathlength of individual trees collected at Marden Park, UK

Inidivual tree cluster greenness (gcc) and pathlength used in the study "UAV-derived greenness and within-crown spatial patterning can detect ash dieback in individual trees".

Data for Stochastically accelerated perturbative triples correction in coupled cluster calculations

This files contains all the data used to perform the plots in the "Stochastically accelerated perturbative triples correction in coupled cluster calculations" article.

Data from: "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal"

Processed datasets used for analysis in "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal" by Hooven et al. published in Mammal Research. R scripts used to process and analyze these data are available from: <a href="https://github.com/nhooven/elk-individual-habitat">https://github.com/nhooven/elk-individual-habitat</a> WS_sampled.csv, SU_sampled.csv, UA_sampled.csv, AW_sampled.csv - Processed telemetry datasets (with relocation data removed), resultant files from script "01 - Pre-processing.R". WS_HRs.csv, SU_HRs.csv, UA_HRs.csv, AW_HRs.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by individual.  WS_groups.csv, UA_groups.csv, AW_groups.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by groups.  Note: Raw telemetry data and home range polygons are not available due to the sensitive nature of providing animal locations publicly. Please direct any questions or concerns to the corresponding author (nathan.d.hooven@gmail.com).

opencc-by-4.0Feb 2024View details →

Connectivity modelling identifies sources and sinks of coral recruitment within reef clusters

This study uses biophysical modelling to estimate coral larval connectivity estimates within the Moore Reef cluster, northern Great Barrier Reef, for the annual spawning events of Acropora corals in 2015, 2016 and 2017. Moore_2015_simple.nc, Moore_2016_simple.nc and Moore_2017_simple.nc contain the hydrodynamic data for the Moore Reef cluster. Moore_grid.nc contains the Moore Reef cluster grids data and Moore_spatial.csv contain the centroids of spatial polygons in the Moore Reef cluster. sysdata.rda contains GBR1 grids data, u_spring.rds and u_gbr1.rds contain mean surface velocities for Moore Reef cluster and GBR1 domain. The transfer probability matrix data contain the connectivity matrices for the days and years that were considered.

opencc-by-4.0Nov 2020View details →

Data to accompany "Automatic text clustering for audio attribute elicitation experiment responses", AES 143rd Convention, New York, NY, USA, 2017

This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1) and the BBC as part of the BBC Audio Research Partnership. Details about the data underlying this work, along with the terms for data access, are available from http://dx.doi.org/10.15126/surreydata.00841589. If you use the data, please cite the following paper: J. Francombe, T. Brookes, and R. Mason, “Automatic text clustering for audio attribute elicitation experiment responses”, AES 143rd Convention, New York, NY, USA, 2017

Dataset for "Engineering defect clustering in diamond-based materials for technological applications via quantum mechanical descriptors"

The unique set of extreme physical properties makes diamond an ideal candidate for applications in the energy industry such as in high-power and high-frequency electronics as well as in electrochemistry and photovoltaics. Furthermore, dopant-vacancy complexes in diamond can be exploited for further development of quantum computers, single-photon emitters, high-precision magnetic field sensing and nanophotonic devices. While certain dopant-vacancy complexes are well-studied, studies of other dopant/vacancy clusters are focused mostly on defect detection while investigations on how to tune their electronic and optical properties for specific applications is mostly omitted. To this aim, we attempted to reveal coupled structural-electronic features and their effect on the band gap of such defects through first principle calculations. We investigated four different defect types: a) dopant-vacancy complexes (X-V), b) two dopants as nearest neighbours (X-X), c) two dopants separated by one carbon atom (X-C-X) and d) two dopants separated by a vacancy (X-V-X). For each of these configurations, we considered Al, B, N, P and Si as dopant atoms. This dataset contains input files needed to reproduce every ground state geometry used in our study.

opencc-by-4.0Mar 2023View details →

SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment

SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.

Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift

Database of catalogs and numerical results for the paper: Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift.   ABSTRACT Hierarchical black hole (BH)  mergers are one of the most straightforward mechanisms producing BHs inside and above the pair-instability mass gap. We investigated the impact of globular cluster (GC) evolution on hierarchical mergers, accounting for the uncertainties related to BH mass pairing functions on the predicted primary BH mass, mass ratio, and spin distribution.  We find that the evolution of the host GC  quenches the hierarchical BH assembly at the third generation, mainly due to cluster expansion powered by a central BH subsystem. Hierarchical mergers match the primary BH mass distribution from GW events for $m_1 > 50 \, \msun$ regardless of the assumed BH pairing function.  At lower masses, however, different pairing functions lead to dramatically different predictions on the primary BH mass merger-rate density.  We find that the primary BH mass distribution evolves with redshift, with a larger contribution from mergers with $m_1 \geq 30 \, \msun$ for $z\geq{}2$. Finally, we calculate the mixing fraction of binary black holes (BBHs) from GCs and isolated binary systems. Our predictions are very  sensitive to the spins, which favor a large fraction ($>0.6$) of BBHs born in GCs in order to reproduce misaligned spin observations.   FILES DESCRIPTION: Files Catalogs.zip contain the data used in this paper.  The directory Metallicities contains the outputs of the Fastcluster runs at Z=0.0002. For each model and for each GC evolutionary case, we report the populations of BBHs at first ("first_generation.csv") and nth ("nth_generation.csv") generation.  The directory Merger_Rate_Density contains the catalogs from Cosmorate+Fastcluster at redshift 0 to 4 ("redshift_*.csv") and the merger rate density as a funcion of redshift ("merger_rate_density.csv"), for different GC models. Also, it contains the mixing fractions for all the models presented in this paper ("mixing_fractions.csv"). The Jupyter notebooks generate the Figures in the main body of the paper.

Fig. 5. A in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis

Fig. 5. A dendrogram illustrating the relationships among the seven Indonesian hornbill species based on 14 morphometric characters, constructed using the Average Linkage model. Legend: Aa = Anthracoceros albirostris, Am = Anthracoceros malayanus, Ru = Rhyticeros undulatus, Rp = Rhyticeros plicatus, Ac = Aceros cassidix, Br = Buceros rhinoceros, dan Bb = Buceros bicornis.

Fig. 1 in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis

Fig. 1. Hornbill genus grouping based on a combination of body length characters (PC1) and beak characters (PC3): A — genus Rhyticeros; B — genus Buceros; C — genus Anthracoceros.

Fig. 3 in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis

Fig. 3. The combination of tail length and head length of two hornbill species within the genus Anthracoceros.

Fig. 4 in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis

Fig. 4. The combination of head length and tail length of three hornbill species within the genus Rhyticeros.

opencc-zeroJun 2024View details →

The data for Newly Detected Old Galactic Globular Cluster Candidates Using Gaia DR3

<pre> </pre> GAIA_input - Raw data downloaded by GAIA GAOSI - Gaussian interpolation and testing AND concer fits correlational graph values The result after clustering_output mh_gspphot - [Fe/H]  ag_gspphot - AG  m_M - distance modulus  - This is also the header of GC01.csv and GC02.csv under .data\gaosi\GAOS,  which are copied from the csv file in .data\gaosi\GAOSI\output2 Because GAIA lacked part of the data of [Fe/H] and AG after clustering, we adopted the Gaussian RV completion method of Qin et al. (2023) doi: 10.3847/1538-4365/acadd6 to supplement the data of [Fe/H] and AG. The data in these 3 folders is the result of the process we performed. .data\gaosi\GAOSI\output and .data\gaosi\GAOSI\output1 and .data\gaosi\GAOSI\output2  .data\gaosi\GAOSI\output3  - Test the data after we finish the Gaussian interpolation .data\gaosi\GAOSI\medain.csv -The median of the data before Gaussian interpolation .data\gaosi\GAOSI\pyngc01_1sigma.csv and .data\gaosi\GAOSI\pyngc02_1sigma.csv -The mean, standard deviation, and error range of m_M and mh_gspphot and ag_gspphot

opencc-by-4.0Dec 2019View details →

Fig. 3 in Cluster Analysis of Non-conserved Proteins of Trypanosoma cruzi Reference Strains Displays Parity between these Groupings (Peptidemes) and the Consensually Accepted Parasite Lineages

Fig. 3. Phenogram of the peptidemes (P) of eight Trypanosoma cruzi reference strains obtained using the SM coefficient and the UPGMA clustering algorithm, based on data from non-conserved proteins, as seen in SDS-PAGE analysis. The major peptidemes are indicated as mP 1 and mP 2. Their subgroups are identified on the right (P II, P VI, P I), and were numbered following their respective genetic types (TcII, TcVI, TcI), as currently used.

opencc-by-4.0Dec 2019View details →

Fig. 1 in Cluster Analysis of Non-conserved Proteins of Trypanosoma cruzi Reference Strains Displays Parity between these Groupings (Peptidemes) and the Consensually Accepted Parasite Lineages

Fig. 1. Total protein profiles of eight Trypanosoma cruzi reference strains separated in 10% SDS-PAGE at 250 V, 25 mA, 90 min, and stained by Coomassie brilliant blue. The position of some conserved proteins is indicated on the right. M: molecular mass markers. (kDa) are indicated on the left.

opencc-by-4.0Dec 2019View details →

Fig. 2 in Cluster Analysis of Non-conserved Proteins of Trypanosoma cruzi Reference Strains Displays Parity between these Groupings (Peptidemes) and the Consensually Accepted Parasite Lineages

Fig. 2. Diagrammatic representation of the twenty-two protein bands not shared by all Trypanosoma cruzi reference strains (nonconserved proteins), as visualized in SDS-PAGE. These bands were coded and analyzed by numerical taxonomy procedures. At the top is indicated the number of the major groups they belong, as identified by different approaches. The bands that were exclusive of one or more strains were highlighted with rectangles. M: molecular mass markers. (kDa) are indicated on the left.